About

I am currently a faculty member in Artificial Intelligence at Duke University, director of the Duke TRUST Lab, and Executive Director of Tensor & Trust. As a leader in bridging the gap between research and industry in machine learning, I have led projects and developed algorithms for the largest companies in the world. More importantly, I have built algorithms that have meaningful impacts - from helping people walk to noninvasively monitoring glucose.

Research topics

• Artificial Intelligence
• Computer Science
• Mathematics
• Machine Learning
• Telecommunications
• Neuroscience
• Statistics
• Medicine
• Data science
• Nanotechnology
• Materials science
• Optoelectronics
• Embedded system
• Human–computer interaction
• Biomedical engineering

Selected publications

• Flexible, high-resolution thin-film electrodes for human and animal neural research

  Journal of Neural Engineering · 2021 · 51 citations

  • Computer Science
  • Materials science
  • Artificial Intelligence

  Our LCP-TF electrodes will advance human neuroscience research and improve clinical care by enabling broad access to transformative, high-resolution electrode arrays.

  PublisherOA PDFDOI

• Investigating sources of inaccuracy in wearable optical heart rate sensors

  npj Digital Medicine · 2020 · 623 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. Accuracy of wearable technologies has been a hotly debated topic in both the research and popular science literature. Currently, wearable technology companies are responsible for assessing and reporting the accuracy of their products, but little information about the evaluation method is made publicly available. Heart rate measurements from wearables are derived from photoplethysmography (PPG), an optical method for measuring changes in blood volume under the skin. Potential inaccuracies in PPG stem from three major areas, includes (1) diverse skin types, (2) motion artifacts, and (3) signal crossover. To date, no study has systematically explored the accuracy of wearables across the full range of skin tones. Here, we explored heart rate and PPG data from consumer- and research-grade wearables under multiple circumstances to test whether and to what extent these inaccuracies exist. We saw no statistically significant difference in accuracy across skin tones, but we saw significant differences between devices, and between activity types, notably, that absolute error during activity was, on average, 30% higher than during rest. Our conclusions indicate that different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity. This has implications for researchers, clinicians, and consumers in drawing study conclusions, combining study results, and making health-related decisions using these devices.

  DOI

• Sufficient sampling for kriging prediction of cortical potential in rat, monkey, and human µECoG

  Journal of Neural Engineering · 2020 · 22 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  . PAC spacing accounted for the effect of signal-to-noise on prediction quality and was sensitive to the full distribution of non-stationary covariance states. Our results show that µECoG arrays should sample at sub-millimeter resolution for applications in diverse cortical areas and for noise resilience.

  PublisherOA PDFDOI

• Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)

  npj Digital Medicine · 2020 · 490 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

  DOI

Frequent coauthors

• Jessilyn Dunn

  Duke University

  30 shared

• Jonathan Viventi

  Duke University

  12 shared

• Chia‐Han Chiang

  Duke University

  12 shared

• Michael Trumpis

  Duke University

  11 shared

• Jinghua Li

  Jilin University

  9 shared

• Amy L. Orsborn

  University of Washington

  8 shared

• Jamie Coleman

  University of Louisville

  7 shared

• John A. Rogers

  Northwestern University

  7 shared

Labs

• TRUST LabPI

  Meet the interdisciplinary TRUST Lab team and collaborators.

Education

• PhD Biomedical Engineering, Biomedical Engineering

  Duke University

  2021

• B.S. Biomedical Engineering, Biomedical Engineering

  North Carolina State University

  2016